scholarly journals A Genetic Algorithm for Finding a Small and Diverse Set of Recent News Stories on a Given Subject: How We Generate AAAI’s AI-Alert

2019 ◽  
Vol 33 ◽  
pp. 9357-9364
Author(s):  
Joshua Eckroth ◽  
Eric Schoen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Supervised Learning ◽  
A Priori ◽  
News Stories ◽  
Scoring Algorithm ◽  
Representative Selection ◽  
Story Selection ◽  
Selection Of

This paper describes the genetic algorithm used to select news stories about artificial intelligence for AAAI’s weekly AIAlert, emailed to nearly 11,000 subscribers. Each week, about 1,500 news stories covering various aspects of artificial intelligence and machine learning are discovered by i2k Connect’s NewsFinder agent. Our challenge is to select just 10 stories from this collection that represent the important news about AI. Since stories and topics do not necessarily repeat in later weeks, we cannot use click tracking and supervised learning to predict which stories or topics are most preferred by readers. Instead, we must build a representative selection of stories a priori, using information about each story’s topics, content, publisher, date of publication, and other features. This paper describes a genetic algorithm that achieves this task. We demonstrate its effectiveness by comparing several engagement metrics from six months of “A/B testing” experiments that compare random story selection vs. a simple scoring algorithm vs. our new genetic algorithm.

A Survey on Intelligence Tools for Data Analytics

2021 ◽  
pp. 73-95
Author(s):  
Shatakshi Singh ◽  
Kanika Gautam ◽  
Prachi Singhal ◽  
Sunil Kumar Jangir ◽  
Manish Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Real Life ◽  
Learning Tools ◽  
The Core ◽  
Training Mode ◽  
Real Life Situation ◽  
Selection Of

The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.

scholarly journals Machine learning and automatic selection of attributes for the identification of Chagas disease from clinical and sociodemographic data

2021 ◽  
Vol 10 (4) ◽  
pp. e19310413879
Author(s):  
Weber de Santana Teles ◽  
Aydano Pamponet Machado ◽  
Paulo Celso Curvelo Cantos Júnior ◽  
Cláudia Moura de Melo ◽  
Maria Hozana Santos Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Chagas Disease ◽  
Disease Diagnosis ◽  
Forward Selection ◽  
Treatment And Prevention ◽  
Sociodemographic Data ◽  
Automatic Selection ◽  
Sensitivity Specificity ◽  
Selection Of

Objective: evaluate the potential use of machine learning and the automatic selection of attributes in discrimination of individuals with and without Chagas disease based on clinical and sociodemographic data. Method: After the evaluation of many learning algorithms, they have been chosen and the comparison between neural network Multilayer Perceptron (MLP) and the Linear Regression (LR) was done, seeking which one presents the best performance for prediction of the Chagas disease diagnosis, being used the criteria of sensitivity, specificity, accuracy and area under the ROC curve (AUC). Generated models were also compared, using the methods of automatic selection of attributes: Forward Selection, Backward Elimination and genetic algorithm. Results: The best results were achieved using the genetic algorithm and the MLP presented accuracy of 95.95%, 78.30% sensitivity, and specificity of 75.00% and AUC of 0.861. Conclusion: It was proved to be a very interesting performance, given the nature of the data used for sorting and use in public health, glimpsing its relevance in the medical field, enabling an approximation of prevalence that justifies the actions of active search of individuals Chagas disease patients for treatment and prevention.

scholarly journals Inverse Problems and Artificial Intelligence

2021 ◽  
pp. 33-43
Author(s):  
С.И. Кабанихин
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
A Priori ◽  
Stability And Convergence ◽  
Computational Algorithms ◽  
Conditional Stability ◽  
A Priori Information ◽  
Sensitivity Data ◽  
Priori Information ◽  
The Relationship

В данной работе приведен анализ взаимосвязей теории обратных и некорректных задач и математических аспектов искусственного интеллекта. Показано, что при анализе вычислительных алгоритмов, которые условно можно отнести к вычислительному искусственному интеллекту (машинное обучение, природоподобные алгоритмы, методы анализа и обработки данных), возможно, а подчас и необходимо, использовать результаты и подходы, развитые в теории и численных методах решения обратных и некорректных задач, такие как регуляризация, условная устойчивость и сходимость, использование априорной информации, идентифицируемость, чувствительность, усвоение данных. This paper analyzes the relationship between the theory of inverse and incorrect problems and the mathematical aspects of artificial intelligence. It is shown that computational algorithms that can be categorized as computational artificial intelligence (machine learning, nature-like algorithms, data analysis and processing) can or should be analyzed with the approaches developed for the theory and numerical methods for solving inverse and incorrect problems. They are regularization, conditional stability and convergence, the use of a priori information, identifiability, sensitivity, data assimilation.

scholarly journals A Supervised Learning Algorithm to Forecast Weather Conditions for Playing Cricket

2019 ◽  
Vol 9 (1) ◽  
pp. 1560-1565
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Redundant Data ◽  
Classification Technique

Now days, Machine learning is considered as the key technique in the field of technologies, such as, Internet of things (IOT), Cloud computing, Big data and Artificial Intelligence etc. As technology enhances, lots of incorrect and redundant data are collected from these fields. To make use of these data for a meaningful purpose, we have to apply mining or classification technique in the real world. In this paper, we have proposed two nobel approaches towards data classification by using supervised learning algorithm

scholarly journals Predictive Analysis of Taxi Fare using Machine Learning

2020 ◽  
pp. 373-378
Author(s):  
Pallab Banerjee ◽  
Biresh Kumar ◽  
Amarnath Singh ◽  
Priyeta Ranjan ◽  
Kunal Soni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Supervised Learning ◽  
Analysis Of Algorithms ◽  
Learning Technologies ◽  
Predictive Analysis ◽  
Prediction Problem ◽  
Prediction Modeling

This research aims to study the predictive analysis, which is a method of analysis in Machine Learning. Many companies like Ola, Uber etc uses Artificial Intelligence and machine learning technologies to find the solution of accurate fare prediction problem. We are proposing this paper after comparative analysis of algorithms like regression and classification, which are useful for prediction modeling to get the most accurate value. This research will be helpful to those, who are involved in fare forecasting. In previous era, the fare was only dependent on distance, but with the enhancement in technologies the cab’s fare is dependent on a lot of factors like time, location, number of passengers, traffic, number of hours, base fare etc. The study is based on Supervised learning whose one application is prediction, in machine learning.

Machine Learning and Data Mining in Bioinformatics

2012 ◽  
pp. 695-703
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

Machine Learning and Data Mining in Bioinformatics

2009 ◽  
pp. 612-621 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

scholarly journals Identifying Benchmarks for Failure Prediction in Industry 4.0

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 68
Author(s):  
Mouhamadou Saliou Diallo ◽  
Sid Ahmed Mokeddem ◽  
Agnès Braud ◽  
Gabriel Frey ◽  
Nicolas Lachiche
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Model ◽  
Supervised Learning ◽  
Industry 4.0 ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Selection Of

Industry 4.0 is characterized by the availability of sensors to operate the so-called intelligent factory. Predictive maintenance, in particular, failure prediction, is an important issue to cut the costs associated with production breaks. We studied more than 40 publications on predictive maintenance. We point out that they focus on various machine learning algorithms rather than on the selection of suitable datasets. In fact, most publications consider a single, usually non-public, benchmark. More benchmarks are needed to design and test the generality of the proposed approaches. This paper is the first to define the requirements on these benchmarks. It highlights that there are only two benchmarks that can be used for supervised learning among the six publicly available ones we found in the literature. We also illustrate how such a benchmark can be used with deep learning to successfully train and evaluate a failure prediction model. We raise several perspectives for research.

scholarly journals Survey on computational intelligence based image encryption techniques

2020 ◽  
Vol 19 (3) ◽  
pp. 1428
Author(s):  
Thirumalaimuthu Ramanathan ◽  
Md. Jakir Hossen ◽  
Md. Shohel Sayeed ◽  
Joseph Emerson Raja
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Image Encryption ◽  
Computational Intelligence ◽  
Visual Cryptography ◽  
Machine Learning Algorithms ◽  
Artificial Intelligence Techniques ◽  
Cryptographic Keys

Image encryption is an important area in visual cryptography that helps in protecting images when shared through internet. There is lot of cryptography algorithms applied for many years in encrypting images. In the recent years, artificial intelligence techniques are combined with cryptography algorithms to support image encryption. Some of the benefits that artificial intelligence techniques can provide are prediction of possible attacks on cryptosystem using machine learning algorithms, generation of cryptographic keys using optimization algorithms, etc. Computational intelligence algorithms are popular in enhancing security for image encryption. The main computational intelligence algorithms used in image encryption are neural network, fuzzy logic and genetic algorithm. In this paper, a review is done on computational intelligence-based image encryption methods that have been proposed in the recent years and the comparison is made on those methods based on their performance on image encryption.

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